Method, system, device and medium for querying product history

ABSTRACT

A method for querying a product history is disclosed. The method includes receiving a product query request including at least one product query parameter for a target product to a product graph database that stores a relational map constructed based on a manufacturing process of the target product and describing entities including product entities and manufacturing entities and entity relations therebetween involved in the manufacturing process, querying the product graph database according to the product query parameter to obtain product history data of the target product by searching for a product entity corresponding to the target product as a target product entity in the relational map according to the parameter, searching for associated manufacturing entities of the target product entity according to the entity relations, obtaining the product history data based on the associated manufacturing entities, and sending a notification message to notify obtained product history data.

RELATED APPLICATION

The present application claims the benefit of Chinese Patent ApplicationNo. 202011174965.4 filed on Oct. 28, 2020, the entire disclosures ofwhich are incorporated herein by reference.

TECHNICAL FIELD

The disclosure relates to the technical field of industrial big data, inparticular to a method, system, device and medium for querying a producthistory.

BACKGROUND

A manufacturing execution system (MES) stores a manufacturing history ofa product which records in detail manufacturing information of theproduct during fabrication. With the manufacturing history of theproduct, the source of a defective product can be traced and the rangeof product recalls can be narrowed, and thus losses caused by recalledproducts can be reduced. A traceable data model can not only record dataabout the manufacturing procedure completely, but also extend to aspectssuch as quality tracing and purchase tracing, which is of greatimportance to the enterprise manufacturing procedure control and themanufacturing procedure improvement.

Product history tracing is usually based on data stored in a relationaldatabase, but for a product history tracing based on a relationaldatabase, a query procedure has problems such as time consuming orcomplicated, which leads to a low efficiency of the defective producttracing.

SUMMARY

In light of the above problems or defects in the prior art, it isdesirable to provide a method, system, apparatus and medium for queryinga product history so as to improve the query efficiency of producthistory data.

In a first aspect, a method for querying a product history is providedaccording to the embodiments of this disclosure, the method comprising:receiving a product query request to a product graph database from aquery device, the product query request including at least one productquery parameter for a target product, the product graph database storinga relational map constructed based on a manufacturing process of thetarget product, the relational map describing entities including productentities and manufacturing entities involved in the manufacturingprocess and entity relations between the entities; querying the productgraph database according to the product query parameter to obtainproduct history data of the target product, comprising: searching for aproduct entity corresponding to the target product as a target productentity in the relational map according to the product query parameter;searching for one or more associated manufacturing entities of thetarget product entity according to the entity relations described by therelational map; obtaining the product history data based on theassociated manufacturing entities; and sending a notification message tothe query device to notify it of the obtained product history data.

In a second aspect, a system for querying a product history is providedaccording to the embodiments of this disclosure, the system comprising:a product graph database, configured to store a relational mapconstructed based on a manufacturing process of a target product, therelational map describing entities including product entities andmanufacturing entities involved in the manufacturing process and entityrelations between the entities; a product history query apparatus,configured to receive a product query request including at least oneproduct query parameter for the target product, query the product graphdatabase according to the product query parameter to obtain producthistory data corresponding to the target product, comprising: search fora product entity corresponding to the target product as a target productentity in the relational map according to the product query parameter;search for one or more associated manufacturing entities of the targetproduct entity according to the entity relations described by therelational map; obtain the product history data based on the associatedmanufacturing entities.

In a third aspect, an electronic device is provided according to theembodiments of this disclosure. The electronic device comprising amemory, a processor and a computer program stored on the memory andexecutable on the processor, the processor being configured to implementthe method described in the embodiments of this disclosure whenexecuting the program.

In a fourth aspect, a computer-readable storage medium is providedaccording to the embodiments of this disclosure, which has a computerprogram stored thereon. The computer program, when executed by aprocessor, causes the processor to implement the method described in theembodiments of this disclosure.

BRIEF DESCRIPTION OF DRAWINGS

By reading detailed description of the nonrestrictive embodiments withreference to the drawings below, other features, goals and advantages ofthis disclosure will become more obvious:

FIG. 1 shows a schematic view of an application scenario of a producthistory query solution according to an embodiment of this disclosure;

FIG. 2 shows an exemplary flow chart of a product history query methodaccording to an embodiment of this disclosure;

FIG. 3 shows an exemplary flow chart of a construction method of aproduct graph database according to an embodiment of this disclosure;

FIG. 4a shows an exemplary representation of an ontology correspondingto the constructed relational map according to an embodiment of thisdisclosure;

FIG. 4b shows an example of partial graph structures of the relationalmap according to an embodiment of this disclosure;

FIG. 5 shows an exemplary flow chart of a further product history querymethod according to an embodiment of this disclosure;

FIG. 6 shows the operation principle of a product history query systemaccording to an embodiment of this disclosure;

FIG. 7 shows an exemplary flow chart of a product history query methodaccording to an embodiment of this disclosure;

FIG. 8a shows a schematic structure view of a product history querysystem according to an embodiment of this disclosure;

FIG. 8b shows a schematic structure view of a product history queryapparatus according to an embodiment of this disclosure;

FIG. 9 shows a schematic structure view of a computer system adapted toimplement a product history query solution according to an embodiment ofthis disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

This disclosure will be further explained in detail in combination withthe drawings and the embodiments. It can be understood that the specificembodiments described herein are only used for explaining relateddisclosure, instead of limiting this disclosure. Besides, it should befurther noted that in order to facilitate the description, only portionsrelated to this disclosure are shown in the drawings.

It should be noted that the embodiments in this disclosure and featuresof the embodiments can be combined with each other where no conflict iscaused. This disclosure will be explained in detail with reference tothe drawings and in combination with the embodiments.

FIG. 1 shows a schematic view of an application scenario of producthistory query according to an embodiment of this disclosure.

As shown in FIG. 1, a user can use a terminal device 101 to query astored product history. The user can query information about a targetproduct such as manufacturing history data (e.g., equipment history,repair history, etc.), or product index, e.g., defect rate and so on, byusing a query client installed in the terminal device. For example, inthe industry of semiconductor display, product history tracing or querycan trace conditions of equipment and units of the equipment throughwhich a product flows in the manufacturing process. The product historydata may comprise detailed information of the equipment through whichthe product flows, including, e.g., temporal information and equipmentparameters when the product flows through the equipment. The producthistory tracing or query can be used for yield analysis. For example,some yield indexes of a product flowing through different equipment areanalyzed so as to analyze the machine error and thus guide the yieldimprovement.

The query client receives a query request inputted by the user and thensends the query request to a server 103, e.g., via a network 102. Thequery request may comprise but is not limited to a product parameter, atarget limit condition and so on. The product parameter comprises but isnot limited to a text type parameter of the product, e.g., a product ID,or an image type parameter of the product, e.g., an actual image takenfor a defective product or an image of a sample corresponding to theproduct, etc. The target limit condition comprises but is not limited toa time limit parameter, e.g., a time range, a location limit parameter,e.g., a location limit range; an equipment group limit parameter, e.g.,an equipment group identifier and so on.

The terminal device 101 may be a mobile device such as a smart phone, atablet computer, smart glasses, etc., and it may also be an electronicdevice such as a desktop computer, but is not limited thereto.

The network 102 comprises but is not limited to a wireless network or awired network, over which standard communication technologies and/orprotocols are used. The network is usually the Internet, and it may alsobe any network, comprising but not limited to any combination of LocalArea Network (LAN), Metropolitan Area Network (MAN), Wide Area Network(WAN), mobile, wired or wireless network, dedicated network or virtualdedicated network.

The server 103 executes a query program based on the query request sentby the terminal device 101 in order to find a result related to thequery request. The result comprises but is not limited to producthistory data, index of interest for the product and so on. The server103 may be an independent physical server, or a server cluster or adistributed system composed of a plurality of physical servers, or acloud server providing basic cloud computing service such as cloudservice, cloud database, cloud computing, cloud function, cloud storage,network service, cloud communication, middleware service, domain nameservice, security service, CDN, and big data and artificial intelligenceplatform.

The server 103 may further comprise a database server. The databaseserver is arranged for storing a database related to product historyquery. In related arts, the database may be a relational database.

In a relational database, information such as defect information of theproduct, equipment history of the product, product coordinate, productID is usually scattered in different data sheets. During the historytracing based on a relational database, interaction with a plurality ofdata sheets is required to retrieve the product history data and theproduct defect information. To take the industry of semiconductordisplay as an example, when joining product defect information, one samepanel may have plural defects, each of which may be caused by pluraldifferent process steps. If each process step is considered as a level,the history tracing or query may involve a distribution relation with upto 3˜4 levels. If the product history data is retrieved according to arelational database, and then the product defect information is analyzedbased on the retrieved product history data, the query data amount willskyrocket.

For example, assuming there is a query request in a form of a compositeinput for 100,000 panels, and each panel has M defects on average, andeach defect has N process segments, and each process segment has Pequipments (EQP) and K units (UNIT), the resultant join data amount mayreach 100000*M*N*(P+K). In other words, the number of the finalresultant records is 100˜1000 times that of the panels. Although onlyone composite query statement is run in the entire query procedure, dataneeds to be correlated by a join operation of plural data sheets in therelational database, which takes a long query time and thus leads to lowquery efficiency.

To solve the above problems, this disclosure proposes a technicalsolution for querying a product history, which may effectively save thequery time and improve the query efficiency of the product history byconstructing a new data system architecture of product history tracing.

FIG. 2 shows an exemplary flow chart of a product history query methodaccording to an embodiment of this disclosure. The method comprises:

Step 201, receiving a product query request to a product graph database,the product query request including at least one product query parameterrelated to a target product.

The product graph database stores a relational map constructed based ona manufacturing process of the product. The relational map describes atleast one of a plurality of manufacturing entities, a plurality ofproduct entities, relations between manufacturing entities, relationsbetween the manufacturing entities and the product entities, andrelations between product entities involved in the manufacturingprocess. In an example, the product graph database comprises arelational map constructed based on the product entities, themanufacturing entities and the relations between the product entitiesand the manufacturing entities.

The relational map may represent the entities, entity relations andentity properties involved in the manufacturing process in the form ofgraphs. The relational map may be a graph search structure constructedbased on entities and triplets. The entities may be manufacturingentities and product entities. The entity relations and the entityproperties may be represented by means of triplets. For example, in aproduct graph database, an entity relation may be stored in the form oftriplet “entity-relation-entity”, and an entity property may be storedin the form of triplet “entity-property-property value”.

The manufacturing entities may refer to manufacturing implementationbodies in respective phases during the manufacturing process from rawmaterials to final products. The manufacturing implementation bodiescomprise but are not limited to factories and stations where theproducts are manufactured, equipment and units for implementingrespective process steps of the products, and so on.

The product entities may refer to products outputted in each phase ofthe manufacturing process, which may have different states and/orshapes. For example, during the manufacturing process of a semiconductordisplay, the product entities comprise but are not limited to glass,half glass and panel. The product entities may also have correspondingproperties. For instance, the properties of the product entities maycomprise defect, product identifier, e.g., product ID, etc.

The relations between manufacturing entities may refer to body relationsbetween the manufacturing implementation bodies. The body relationscomprise but are not limited to a “containing” relation, e.g., a factorycontains a station (factory->station), and a station contains anequipment (station->equipment), and an equipment contains a unit(equipment->unit).

The relations between product entities may refer to product relationsbetween the products. The product relations comprise but are not limitedto a product-related (e.g., cutting) relation, and a relation betweenthe product and properties thereof. For example, the panel is related to(obtained by cutting) a half glass (panel->half glass), a half glass isrelated to (obtained by cutting) a glass (half glass->glass), and apanel is related to a defect (panel->defect), and a panel is related toa product ID (panel->product ID).

The relations between the product entities and the manufacturingentities may refer to product body relations between the product and themanufacturing implementation bodies thereof. The product body relationscomprise but are not limited to a relation of product passing throughequipment. For example, a glass passes through an equipment(glass->equipment), and a glass passes through a unit (glass->unit), anda half glass passes through an equipment (half glass->equipment), and ahalf glass passes through a unit (half glass->unit), and so on.

The product query request may be a request message containing one ormore product query parameters received from a query device. Exemplarily,the product query parameter may comprise: a defect of a certain product,a time range related to a certain product, a product arrival rate of acertain product and so on. For example, an image of a defective productcontained in the request message may be analyzed to obtain the productquery parameter. Alternatively, the query parameter may also be inputteddirectly by a third party or a user. For instance, the user may manuallyinput the range of the production time of a product to be queried. Therequest message may adopt any suitable format and/or carrier form. Insome embodiments, the product query parameter may comprise an identifiercorresponding to the product, e.g., a product ID.

Step 202, querying the product graph database according to the productquery parameter to obtain the product history of the target product.Herein querying the product graph database according to the productquery parameter comprises: searching for a product entity correspondingto the target product as a target product entity in the relational mapaccording to the product query parameter; searching for one or moreassociated manufacturing entities of the target product entity accordingto the entity relations described by the relational map; obtaining theproduct history data based on the associated manufacturing entities.

Optionally, the associated manufacturing entity may be a manufacturingentity having entity relations with the target product entity in therelational map.

Additionally, the product history data may further comprise informationof a product entity corresponding to the target product and/orinformation of a product entity having entity relations with the productentity found in the relational map according to the product queryparameter. The information of entities comprises property information ofthe entities and/or relational information of the entities.

In some embodiments, the method further comprises: after the producthistory data of the target product is queried, a notification messagemay be sent to the query device so as to notify it of the queriedproduct history data.

According to the embodiments of this disclosure, the manufacturingprocess of the product is modeled and described in a structured form byusing the product graph database, which enables rapid and effectivequery of the product history data related to the product queryparameter. Therefore, as compared with a query for a relationaldatabase, the query based on a product graph database according to theembodiments of this disclosure may achieve product history tracing on alarge scale, thereby improving the query speed of the product historygreatly.

In some embodiments, the product query parameter is a parameter relatedto the target product (i.e., the product to be queried). Therefore, aproduct entity of which property information matches with the productquery parameter may be searched as the target product entity accordingto the entities described by the relational map. Optionally, informationof the target product entity stored in the product graph database may becomprised in the product history data. Additionally or alternatively, itis also possible to search for a product entity of which relatedproperty information matches with the product query parameter as a firstproduct entity according to the entities or entity relations describedby the relational map, search for a second product entity related to thefirst product entity according to the product entity relations betweenthe product entities described by the relational map, and determine thesecond product entity as the target product entity. In some embodiments,the product entity relation between the second product entity and thefirst product entity is correlating relation, which may indicate that afirst product corresponding to the first product entity is made from asecond product corresponding to the second product entity. Exemplarily,a second process step for fabricating the second product entity in themanufacturing process is upstream or on a upper-level above a firstprocess step for fabricating the first product entity, so after thesecond product is fabricated, the first product is made from the secondproduct. Accordingly, the second product entity may be called anupper-level product entity of the first product entity. For example, inthe manufacturing process of a semiconductor display, the panel may be afirst product entity, and the glass may be a second product entity, andthe glass is an upper-level product entity of the panel.

The product query parameter may comprise but is not limited to at leastone of a product identifier, a time range and a product arrival rate.The time range may comprise a start time and an end time for example, ora start time and a time duration set based on the start time.

In an example, when the product query parameter is a product identifier,the corresponding query may be: searching the relational map for aproduct entity of which property information matches with the productidentifier as the target product entity, and using information of one ormore associated manufacturing entities of the product entity as producthistory data. For example, the product history data may be obtained bycombining information of manufacturing entities having entity relationswith the product entity. For instance, the product history data isobtained by combining information of manufacturing entities having a“passing” relation with the product entity (i.e., equipments throughwhich the product passes). Optionally, these manufacturing entities mayalso be combined according to temporal property information of themanufacturing entities.

In a further example, when the product query parameters comprise aproduct arrival rate, a time range and a product identifier, a combinedquery may be carried out based on these product query parameters, e.g.,the product history of a target entity having the product identifier andmeeting a requirement of the product arrival rate with a production timefalling within the time range is queried. The product arrival rate maybe used for screening a product sample set to be analyzed, and it may beobtained by calculating a ratio of the number of first product entitiesarriving at a specific station to the total number of the first productentities, wherein the first product entities are obtained from one samesecond product entity. During the manufacturing process of asemiconductor display, the product arrival rate may refer to apercentage of panels made from the same glass that arrive at a detectionpoint, e.g., the percentage may reach 90%. Exemplarily, the number ofpanels belonging to a certain glass that pass through a station may becounted by gathering all panels passing through that station into thecertain glass. If the counted number is N, and the glass is cut into Mpanels, the product arrival rate at this station is N/M*100%.

Exemplarily, after a time range parameter and a product arrival rateparameter are acquired, a first relation between product entities in theproduct graph database may be queried according to these parameters,thereby obtaining a first product entity meeting the requirement ofproduct arrival rate parameter. The first relation is for representingentity relations between two product entities in the product graphdatabase. In an example, the first relation represents the first productentity is obtained from/correlated to the second product entity. A ratioof the number of the first product entities arriving at the detectionpoint to the total number of the first product entities may be stored inthe relation property of the first relation. Exemplarily, meeting therequirement of product arrival rate parameter means that the storedratio meets the requirement of product arrival rate parameter.

In an example, the first product entity may be panel for example. Thefirst relation may be Glass←panel, or halfGlass←panel, halfGlass←Glass,i.e., cutting multiple panels from one same glass or half glass.

A second relation between product entities in the product graph databasemay be queried based on the obtained first product entity, therebyobtaining a second product entity. The second product entity may beglass for example. The second relation is for representing a relationbetween a defective result of the first product entity and the secondproduct entity.

For instance, a first product entity panel is determined based on theproduct arrival rate parameter. The product arrival rate parameterindicates a threshold of the product arrival rate. The product arrivalrate may be defined as, after a glass is cut into multiple panels, aratio of the number of the panels arriving at a detection pointsynchronically or in batches to the total number of the panels intowhich the glass is cut. Exemplarily, assuming the threshold of theproduct arrival rate is 80%, when the product arrival rate reaches 80%,it means that 80% of all the panels obtained from the same glass havearrived at the detection point. Now, the second product entity glasscorresponding to the multiple panels arriving at the detection point isdetermined to have met the requirement of the product arrival rateparameter, so the second product entity glass may be used as a targetproduct entity. The target product entity corresponds to a targetproduct for which the product history is queried.

A defective result may be detected when the panels obtained from thesecond product entity glass undergo product detection upon their arrivalat the detection point. A second product entity corresponding to thedefective result may be traced by constructing a relation between thedefective result of the first product entity and the second productentity beforehand in the relational map. A relation between thedefective result of the first product entity and the second productentity may be constructed by performing data extraction on datarelations between multiple panels with defective results and a glass.When multiple panels with defective results are obtained, the defectiveresults may be gathered to a corresponding glass based on the firstrelation between the panel and the glass as mentioned above, therebydetermining a defective glass.

Since such gathering is realized based on a relation pre-constructed inthe product graph database, there is no need to perform a join operationamong multiple linked data sheets, so the query speed of the productgraph database according to the embodiments of this disclosure ispromoted by about two orders of magnitude as compared with a relationaldatabase.

The first product entity and the second product entity may be correlatedwith each other by a simple production process, so after the existenceof a first product entity with a defective result is determined, apossible cause of the defective result may be further found based on asecond product entity correlated therewith. In some scenarios, toacquire a product history of the second product entity may be of greaterimportance in finding possible causes of defects.

Relations between the product entities and the manufacturing entities inthe product graph database are queried according to the second productentity, thereby obtaining a product history corresponding to the secondproduct entity. The relations between the product entities and themanufacturing entities are used for representing relations between thesecond product and the manufacturing implementation bodies thereof.

For example, given that the second product entity is a Glass, themanufacturing entity refers to one or more equipments for manufacturingthe Glass, and the factory where the equipments are located, and so on.

In some embodiments, the target product Glass may be determined based ona product identifier and a product arrival rate. For instance, based ona product property relation established beforehand in the product graphdatabase, e.g., a relation of (panel->product ID), a first productentity corresponding to the product identifier may be obtained. Then asecond product entity may be determined by performing a relation searchaccording to an inverse process of the manufacturing process of thefirst product entity. That is, the second product entity may be obtainedby querying the property relation of the first product entity: relatingto defect (panel->defect) and the entity relation: relating to glass(panel->glass). If a half glass is involved in the manufacturingprocess, the second product entity may be obtained based on the propertyrelation of the first product entity: relating to defect (panel->defect)and the entity relations: relating to half glass (panel->half glass) andrelating to glass (half glass->glass). That is, with the defective panelas a starting point, an inverse search for the Glass is carried out todetermine the Glass corresponding to the defective panel. Thenmanufacturing equipment through which the second product entity Glasspasses during the manufacture is determined based on the entityrelations of the Glass, and then product history data of the secondproduct entity may be obtained.

For example, given that the panel is directly obtained from the Glass,and a manufacturing process relation between the product entity glassand the manufacturing entities (e.g., equipment, unit and so on) may bedetermined from the entity relations of e.g., glass->equipment andglass->unit. Assuming the Glass passes through multiple equipments suchas A, B, C and D during the manufacturing process of the Glass, aconnection relation of a star-shaped structure may be establishedbetween the second product entity and each manufacturing entity (i.e.,abstract entities of apparatuses A, B, C and D) it passes through.Optionally, a temporal property may be added for each manufacturingentity, which temporal property is for recording a start time and an endtime of the passage of the second product entity through themanufacturing entity.

In this way, after the second product entity is determined, a set ofmanufacturing entities may be obtained by searching the connectionrelation(s) of a star-shaped structure. Then a temporal sequence inwhich the second product entity passes through each manufacturing entitymay be determined based on the temporal property of each manufacturingentity, and the product history data of the second product entity may bereadily obtained by outputting the set of manufacturing entities in thetemporal sequence.

According to the embodiments of this disclosure, after the secondproduct entity is determined based on the defective result, a set ofmanufacturing entities related to the second product entity is obtainedby means of the entity relations pre-constructed between the secondproduct entity and the manufacturing entities in the relational map,which effectively shortens the time taken to acquire the product historydata. As compared with a relational database in which it is required tojoin multiple related data sheets in order to query the product historydata during the search for defective information of the product throughjoin, the embodiments of this disclosure may trace and query the producthistory data corresponding to the second product entity by using therelational map stored in the product graph database, which not onlyeffectively saves the query time but also allows to visually present theproduct history data.

FIG. 3 shows an exemplary flow chart of a construction method of aproduct graph database according to an embodiment of this disclosure.The construction procedure of a product graph database may comprise anoff-line constructing process and an on-line updating process.

The off-line constructing process may comprise:

Step 301, obtaining original product history data from a manufacturingexecution system MES executing the manufacturing process;

Step 302, processing the original product history data to obtain theproduct entities and the manufacturing entities, and constructing theentity relations between the product entities and the manufacturingentities based on transfer relations of the original product historydata. The transfer relations may refer to the relations between aproduct and respective manufacturing implementation entities that areincurred by transfer or circulation of the product among themanufacturing implementation entities according to a manufacturingworkflow. For example, if a glass GLASS A passes through a unit UNIT1under an equipment EQP1, the original product history data obtained maycomprise information about the passage of GLASS A through UNIT1, andthereby a “passing” relation between the product entity GLASS A and themanufacturing entity EQP1 may be constructed in the relational map,i.e., the relation of GLASS A passing through UNIT1.

Step 303, constructing a relational map based on the product entities,the manufacturing entities and the entity relations to obtain theproduct graph database.

The original product history data may comprise metadata and operationaldata. A source end is a location where the metadata is stored and alocation (here is an operational data store, ODS for short) where theoperational data is stored in the MES.

The processing of the original product history data may comprise loadingthe original product history data from the source end (i.e., the MES) toa destination end (i.e., the product graph database) after dataextraction and transformation (including data cleaning).

FIG. 4a exemplarily shows an ontology corresponding to a constructedrelational map described in OWL (Ontology Web Language), which mainlydescribes the relations between one entity and another involved in therelational map. As shown in FIG. 4a , class Thing describes entitiesinvolved in the relational map, comprising for example: Product Entity(ProductEntity), Equipment Entity (EquipmentEntity) and Defect. TheProduct Entity comprises Glass, Half Glass (HalfGlass) and Panel. TheEquipment Entity comprises Equipment, Station and Unit. Top ObjectProperty (topObjectProperty) describes relational properties in therelational map, comprising for example: containDefect, passEquipment,contains (containUNIT, containEquipment), relateGlass andrelateHalfGlass and so on.

FIG. 4b shows an example of partial graph structure of the relationalmap according to an embodiment of this disclosure. The graph structureis a star-shaped structure, wherein nodes indicated by dots represententities, and sides between the nodes indicated by straight lines witharrows represent entity relations and words on the sides represent typesof the corresponding entity relations. FIG. 4b shows four types ofmanufacturing entities: factory entities (FACTORY), station entities(STA), equipment entities (EQP) and unit entities (UNIT). LinesC_STATION, C_EQP and C_UNIT with arrows represent that the entityrelations between the factory entity and the station entity, the stationentity and the equipment entity, the equipment entity and the unitentity are respectively a containing relation. For example, the graphstructure shows that factory entity 410 contains (C_STATION) stationentity 420, station entity 420 contains (C_EQP) equipment entity 430,and equipment entity contains (C_UNIT) unit entity 440.

In some embodiment, in the construction of a product graph database,speed of ETL and query may be effectively promoted by establishing anindex on specific properties of the entities or the entity relations.For example, an index of data transfer relation may be established inproperty parameters of the relations between manufacturing entities, therelations between manufacturing entities and product entities, and therelations between product entities, so as to rapidly query the transferstate of a product.

In some other embodiments, an identifier property may also be added forthe entities and/or entity relations, which identifier property is foruniquely identifying each entity and entity relation. For example, aunique identifier (ID) property may be added for a product entity ormanufacturing entity and an index may be established for the IDproperty. In this way, when updating data for some entities, inparticular when the data amount is huge, the speed of data insertion maybe promoted significantly by searching the product graph database forthese entities based on the ID index. Exemplarily, the product entitypanel may have a property of Panel.productID. After an index isestablished for the property of Panel.productID, use of a single productID for a history data query may rapidly decrease the data amount ofpanels to be scanned, thereby promoting the query speed.

In some other embodiments, since most of the history queries need toperform screening based on a time range, it is possible to add atemporal property for the entities and/or the entity relations, andestablish an index for the temporal property. For example, in theprocess of product history data query based on a need of defectanalysis, a user may select panels to be traced based on a time range.Since the product entity panel has a temporal property Panel.date, afterthe establishment of an index for the temporal property Panel.date,performing a query based on a time range may significantly decrease thedata amount of panels to be scanned and thereby reduce the query time.

The on-line updating process may comprise: when the entities and/or theentity relations extracted from the original product history data arechanged with respect to the relational map stored in the product graphdatabase, updating the relational map based on the changed entitiesand/or entity relations.

The original product history data may be extracted from the EMSperiodically or by means of subscription-and-push. In some embodiments,it is possible to subscribe to the MES system for a change event of theoriginal product history data such that once the MES system detects achange in the original product history data, it will automatically pushthe changed original product history data.

In some embodiments, after the acquisition of product history data byusing the product graph database, the product history data may beutilized to analyze an index of interest, thereby better extracting keyinformation and further promoting the analysis efficiency of the producthistory data in different applications. FIG. 5 shows an exemplary flowchart of a further product history query method according to anembodiment of this disclosure. The method comprises:

Step 501, receiving a product query request, the product query requestincluding multiple product query parameters;

Step 502, querying a pre-established product graph database according tothe product query parameters to obtain product history datacorresponding to a target product;

Step 503, generating an index of interest for the target product basedon the product history data.

The way of querying the product history data in steps 501 and 502 may bethe same as in the query process described with reference to FIGS. 1-4.Additionally, upon receipt of the product query request, it is possibleto create a query statement according to the product query parameter,and query the product graph database based on the query statement toobtain the product history data.

The query statement may comprise a multi-hop query across multipleentities in the relational map. As used herein, a multi-hop query mayrefer to a query of which a query path comprises at least oneintermediate entity other than a start entity acting as the start pointof the query and a target entity that is the end point of the query. Insuch a scenario, a directly connected entity relation may be establishedbetween the start entity and the end entity, thereby accelerating thequery speed. In some embodiments, a direct connection may be establishedbetween two entities without entity relations when an occurrencefrequency of a multi-hop query is greater than a predeterminedthreshold.

For example, entity relationsPanel-[relateHalfGlass]->HalfGlass-[relateGlass]->Glass initially existin the relational map. There is no entity relation between Panel andGlass. If frequent queries about the history of related Glass of Panelare detected, i.e., a multi-hop query across three entities Panel,HalfGlass and Glass is used frequently, e.g., the occurrence frequencyof the multi-hop query has exceeded a predetermined threshold, therelational map may be modified so as to directly establish connection ofPanel-[R_Glass]->Glass between the entities of Panel and Glass. Actualtests have shown that the connection may accelerate the query speed by20%.

In some embodiments, in the relational map, data may be stored in theentity relations between entities. For example, the start time and theend time of passage of Glass through a certain equipment and/or acertain unit may be stored in the relational properties of Glass. In afurther example, coordinates of defective points and counting ofdefective points may be stored in the relational property of Panelrelate defect. By storing more information, e.g., temporal informationand statistical information, in the properties of the entity relations,more query dimensions and higher query accuracy may be provided.

In some embodiments, step 503 may further comprise: obtaining apredetermined index of interest calculation template; calculating theindex of interest for the target product using the product history dataof the target product according to the index of interest calculationtemplate.

By presetting a template for the calculation of the index of interest,key information about the product tracing may be obtained more rapidlybased on the product history data, which enables a rapider tracing incase of a product quality problem, thereby effectively promoting theproduction efficiency of the EMS system.

The product history query solution according to an embodiment of thisdisclosure will be explained in a scenario of yield analysis by takingthe manufacturing process of a display device as an example. FIG. 6shows the operation principle for a product history query systemaccording to an embodiment of this disclosure. The system comprises aproduct graph database creation part 610 and a query part 620.

The product graph database creation part 610 comprises an ETL module 611and a graph database storage module 612. The query part 620 comprises aquery terminal 621 and a server 622.

The product graph database creation part 610 is arranged for modelingthe manufacturing process of EMS to construct a corresponding relationalmap. In the constructed relational map, the correlating relationsbetween the factories, the manufacturing equipment, the equipment andthe manufactured products of the EMS are organized in the form ofgraphs. In some embodiments, the relational map may comprise a metadatalayer and a data layer. The metadata layer is arranged for constructingrelations between manufacturing entities such as the factories andequipment. Exemplarily, the metadata layer may comprise manufacturingentities such as factories, stations, equipment, and units. The metadatalayer may further comprise relations between the manufacturing entities,e.g., a factory contains a station (factory->station), and a stationcontains equipment (station->equipment), and equipment contains a unit(equipment->unit). The data layer is arranged for constructing relationsrelated to the product entities. Exemplarily, the data layer maydescribe the relations between the product entities and themanufacturing entities as product passing station, and the properties ofthe product entities as product defects, product IDs and so on.

In a scenario of display manufacture, the data layer may involveentities such as glass, half glass, panel, defect and product ID, aswell as corresponding relations, e.g., panel relating to half glass(panel->half glass), half glass relating to glass (half glass->glass),panel relating to defect (panel->defect), panel belonging to product(panel->product ID), glass passing through equipment (glass->equipment),glass passing through unit (glass->unit), half glass passing throughequipment (half glass->equipment), and half glass passing through unit(half glass->unit).

The relations in the relational map may be represented in the form oftriplets. For example, relation “glass passing through equipment” may berepresented by Glass-pass-Equipment, and relation “panel related to halfpanel” may be represented by Panel-relate-HalfGlass.

In some embodiments, when constructing the ontology during theconstruction of the relational map of the manufacturing process, sincesome equipment operations may be required occasionally, it is possibleto choose based on requirements whether to filter out these equipmentoperations, i.e., not to describe the entities or entity relations ofthese equipment operations in the relational map. In some otherembodiments, some product history analyses, e.g., machine erroranalysis, need to be analyzed among manufacturing entities (e.g.,equipment or unit) of a same group, so group information of themanufacturing entities is constructed. In this example, in theconstruction of a relational map, a group property may be added for themanufacturing entities so as to store related group information. In somefurther embodiments, since a product may pass through one samemanufacturing entity (e.g., unit) multiple times in the manufacturingprocess, it is possible to expand a single unit that has been passedthrough multiple times into multiple units in the relational mapaccording to the process steps and add a process step property for theunit so as to distinguish different process steps involving this unit.For example, two aluminum layers are to be made in one process, so onesame unit will be passed through twice. In this way, the unit may berepresented as UNIT 1 in the relational map for the first passage, andbe represented as UNIT 2 for the second passage. As such, this processmay be described as UNIT1-UNIT2, indicating that the first passage isdone by UNIT1 and the second passage is done by UNIT 2.

The ETL module 611 is for loading the acquired original data of abusiness system (e.g., the MES) to the product graph database accordingto the constructed relational map after extraction and transformation.The original data comprises metadata and operational data. The ETLmodule may be configured to update metadata and acquire product data(e.g., operational data ODS) and so on. Exemplarily, the ETL module mayuse Pentaho as an ETL tool, which achieves better expandability andflexibility while accelerating the development.

The graph database storage module 612 is arranged for storing theproduct graph database. In some embodiments, the product graph databasemay adopt a database supporting a property graph model. Optionally,indexes may be established for commonly used queries, e.g., an index maybe established on a query with a high use frequency, so as to supportthe query requirement of real-time analysis.

The query part 620 is arranged for initiating a query to the productgraph database and receiving a returned query result. In someembodiments, the query part 620 further provides a product index ofinterest generated based on the query result.

The query terminal 621 receives product query parameters. The productquery parameters may comprise for example defect of the target product,time range (e.g., the time range of production time) or product arrivalrate related to the target product. These product query parameters maybe manually inputted by the user or preset by the system. The queryterminal 621 will pack the product query parameters in a product queryrequest which will be sent to the server 622.

The query terminal 621 is connected with the server 622 via acommunication network. The query terminal sends a query request to theserver which feeds back a query result to the query terminal based onthe query request.

FIG. 7 shows a flow chart of a product history query method according toan embodiment of this disclosure. The method may be implemented by theserver 622 in FIG. 6. The method comprises:

Step 701, receiving a product query request sent by the query terminal;

Step 702, parsing the product query request to obtain a product queryparameter;

Step 703, invoking a pre-stored query statement template according tothe product query parameter to create a query statement for querying theproduct graph database;

Step 704, querying the product graph database based on the created querystatement to obtain product history data.

Examples of the query statement and the resultant product history dataare given as follows.

First query statement Q1 is used for searching for product history dataof glass related to the panel, and the product query parameter is panelproduct identifier, panel time range and panel defect.

Q1:“Match (panel:PANEL)-[:R_GLASS]->(glass:GLASS)/*search for GLASSrelated to PANEL*/

Optional match (panel)-[:relateDefect]->(dft:DEFECT)/*optionally searchfor defective panel*/

where panel.productID={productID} and {startDate}<=panel.date<={endDate}and dft.ID={dftID}/*panel product identifier is productID, panel timerange falls between startDate and endDate, and defect identifier isdftID*/

with glass as glass, count(panel) as cnt, count(dft) as dftCnt/*forglass, panel count as cnt, defect count as dtfCnt*/match

(glass)-[:PASS]->(eqp:EQP)/*search for equipments through which theglass passes*/

return eqp.group as group, eqp.ID as ID, sum(cnt) as total, sum(dftCnt)as dftCnt”; /*return eqp.group as group, eqp.ID as identifier, sum cntas a total; sum dftCnt as the count of defects*/

The product history data returned by the first query statement Q1comprises equipment group through which the defective panel passes,equipment identifier, panel total, and defective panel total.

Second query statement Q2 is used for searching for product history dataof glass related to the panel, and the product query parameter is panelproduct identifier, panel time range and panel defect.

Q2: “Match (panel:PANEL)-[:R_GLASS]->(glass:GLASS)/*search for GLASSrelated to PANEL*/

Optional match (panel)-[:relateDefect]->(dft:DEFECT)/*optionally searchfor defective panel*/

where panel.productID={productID} and {startDate}<=panel.date<={endDate}and dft.ID={dftID}/*panel product identifier is productID, panel timerange falls between startDate and endDate, and defect identifier isdftID*/return

count(panel) as totalPanel, count(dft) as totalDft”; /*return panelcount as panel total, dft count as defect total*/

The product history data returned by the second query statement Q2comprises panel total, and defective panel total.

Then, the server 622 may send the product history data to the queryterminal 621. The query terminal 621 may visually display the producthistory data.

Optionally, the server 622 may further analyze the product history dataso as to extract key information. For example, an index of interest forthe target product may be generated based on the product history data.

Step 705, calculating the index of interest for the product to bequeried (i.e. the target product) using the product history dataaccording to the pre-stored index of interest calculation template,e.g., the index of interest for defective Glass to be queried. Forexample, the corresponding index of interest calculation template may beacquired based on the product history data, and the index of interestfor the target product may be calculated using the product history dataof the target product according to the index of interest calculationtemplate.

The index of interest calculation template using JsonPath syntax isshown exemplarily as follows:

 {“total”:Q2[0].totalPanel, /*total: obtain panel total by using Q2query statement*/  “dftTotal”:Q2[0].totalDft,/*defect total: obtaindefect total by using Q2 query statement*/  “iv”: {  “group”:Q1.group,/* group: obtain group identifier by using Q1 query statement*/ “eqpID”:Q1.ID, /* equipment identifier: obtain equipment identifier byusing Q1 query statement*/  “iv”:Math.IV(Q2[0].totalPanel,Q2[0].totalDft, Q1[*].dftCnt, Q1[*].total)/*calculate index using paneltotal, defect total, defect count and total*/  }  }

In the calculation template, the product history data comprises thepanel total and the defect total of a certain glass obtained by queryingwith the second query statement as well as the panel total and thedefect total of all glasses corresponding to a certain equipment groupobtained by querying with the first query statement. The calculatedindex of interest is panel defect rate.

Step 706, the server 622 sends the index of interest to the queryterminal 621. For example, the server 622 may include in a notificationmessage the calculated index of interest and send it to the queryterminal.

By calculating the index of interest for the product according to apreset index of interest calculation template, complicated procedure ofsearch and calculation through joining of data sheets in related artsmay be avoided, which effectively promotes the query efficiency of theindex of interest and saves the query time of the index of interest.

Flow charts and block diagrams in the drawings illustrate possiblesystematic architectures, functions and operations that may beimplemented by the system, the method and the computer program productaccording to various embodiments of this disclosure. In this regard,each box in the flow charts or the block diagrams may represent amodule, a program segment or a portion of codes. The module, the programsegment or the portion of codes comprises one or more executableinstructions for implementing prescribed logic functions. It should alsobe noted that in some alternative implementations, the functionsindicated in the boxes may also be performed in a sequence differentfrom that indicated in the drawings. For example, two consecutive boxesmay actually be executed substantially concurrently, and sometimes theymay also be executed in an opposite sequence, which depends on thefunctions involved. It should also be noted that each box in the blockdiagrams and/or the flow charts and a combination of the boxes in theblock diagrams and/or the flow charts, e.g., an ETL module, may beimplemented by means of a dedicated hardware-based system for executingthe prescribed functions or operations, or by means of a combination ofdedicated hardware and computer instructions.

FIG. 8a shows a schematic structure view of a product history querysystem according to an embodiment of this disclosure. The system maycomprise a product graph database 801 and a product history queryapparatus 802.

The product graph database 801 is arranged for storing a relational mapconstructed based on the manufacturing process of a target product. Therelational map describes entities involved in the manufacturing processand entity relations between the entities. The entities include bothproduct entities and manufacturing entities.

The product history query apparatus 802 is arranged for receiving aproduct query request including at least one product query parameter;querying the product graph database according to the product queryparameter to obtain product history data corresponding to the targetproduct. The querying comprises: searching for a product entitycorresponding to the target product as a target product entity in therelational map according to the product query parameter, searching forone or more associated manufacturing entities of the target productentity according to the entity relations described by the relationalmap, and obtaining the product history data based on the associatedmanufacturing entities.

In some embodiments, the product history data is obtained by combiningthe associated manufacturing entities according to a temporal propertyof the entity relations between the target product entity and theassociated manufacturing entities.

In some embodiments, the product history query apparatus 802 isconfigured to create a query statement according to the product queryparameter and search the product graph database according to the querystatement.

The product history query apparatus 802 may be arranged for implementingvarious product history query methods according to the embodiments ofthis disclosure, including the method described in FIG. 2.

In some embodiments, the product history query apparatus 802 isconfigure to search for a product entity corresponding to the targetproduct as a target product entity according to a product identifier;search for based on the target product entity associated manufacturingentities having entity relations therewith; combine the associatedmanufacturing entities according to a temporal property into producthistory data corresponding to the target product.

In some embodiments, the product history query apparatus 802 is arrangedfor querying a first relation between product entities in the productgraph database according to a time range parameter and a product arrivalrate parameter, thereby obtaining a first product entity meeting therequirement of product arrival rate parameter, the first relation beingused for representing the correlating relation between the first productentity and the second product entity. A second relation between productentities in the product graph database is queried based on the firstproduct entity meeting the requirement of product arrival rateparameter, thereby obtaining the second product entity that has thesecond relation with the first product entity. The second relation isused for representing a correlating relation between a defective resultof the first product entity and the second product entity. A thirdrelation between product entities and manufacturing entities in theproduct graph database is queried based on the second product entity,thereby obtaining product history data corresponding to the secondproduct entity. The third relation is used for representing associationrelations between the second product entity and the manufacturingentities.

In some embodiments, the system further comprises a database creationmodule 803 for creating a product graph database. The database creationmodule 803 is configured to obtain original product history data from amanufacturing execution system MES executing the manufacturing process;process the original product history data to obtain the product entitiesand the manufacturing entities, and construct the entity relationsbetween the product entities and the manufacturing entities based ontransfer relations of the original product history data; construct arelational map based on the product entities, the manufacturing entitiesand the relations to obtain the product graph database. In someembodiments, the database creation module 803 may be a database creatingcircuit. Alternatively, the database creation module 803 may also beimplemented by a processor executing a corresponding creation program.

The system may further comprise a database updating module 804. Thedatabase updating module 804 is arranged for updating the product graphdatabase, and configured to: in response to at least one of the entitiesand the entity relations extracted from the original product historydata being changed with respect to the relational map stored in theproduct graph database 801, update the relational map based on thechanged entities and entity relations, and store the updated relationalmap in the product graph database 801. In some embodiment, the databaseupdating module 804 is configured to: in response to the query to theproduct graph database 801 comprising a multi-hop query across aplurality of entities in the relational map and an occurrence frequencyof the multi-hop query being greater than a predetermined threshold,establish a directly connected entity relation between a start queryentity and an end query entity corresponding to a start and an end ofthe multi-hop query in the relational map.

In some embodiments, the database updating module 804 may be a databaseupdating circuit. Alternatively, the database updating module 803 mayalso be implemented by a processor executing a corresponding updatingprogram.

The product history query apparatus 801 is further configured to createa query statement for querying the product graph database according tothe product query parameters; querying the product graph database basedon the query statement to obtain corresponding product history data ofthe target product.

The product history query apparatus 801 is further configured tocalculate an index of interest for the target product based on theproduct history data.

The product history query apparatus 801 is further configured tocalculate an index of interest for the target product using the producthistory data of the target product according to an index of interestcalculation template.

In a scenario of product yield analysis, the resultant index oftendepends on a multi-level gathering value. The system according to theembodiments of this disclosure stores a relational map corresponding tothe manufacturing process by using a product graph database, whichallows direct gathering of defect information to an upper-level productaccording to multiple relations between the product to be queried andthe corresponding upper-level product during the query of producthistory data, thereby shortening the time taken to acquire the producthistory data of the upper-level product.

The gathering of this disclosure is realized based on a relationpre-constructed in the product graph database, so no join operation isneeded among multiple linked data sheets, and thus the query speed ispromoted by about two orders of magnitude as compared with a relationaldatabase.

FIG. 8b shows a schematic structure view of a product history queryapparatus according to an embodiment of this disclosure. The producthistory query apparatus may be implemented in a query terminal or aserver. The product history query apparatus comprises: a templatestorage 8011, a query request processor 8012 and a result parsingprocessor 8013.

The template storage 8011 is arranged for storing a query statementtemplate and/or an index of interest calculation template. The querystatement template defines an index to be queried, and it may be writtenin a Cypher or Gremlin language. The index of interest calculationtemplate defines a calculation method for calculating the query resultreturned by the product graph database 802. The template storage isarranged for coordinating the user's query request, the interaction withthe product graph database and the parsing manner of the query result.

The query request processor 8012 is arranged for receiving a productquery request and requesting/invoking a query statement template fromthe template storage 8012 according to a product query parameter,creating a query statement using the product query parameter based onthe query statement template, and querying the product graph database802 according to the query statement. In some embodiments, the queryrequest processor 8012 may receive product history data corresponding tothe product to be queried and fed back by the product graph database.

The result parsing processor 8013 is arranged for receiving producthistory data fed back by the product graph database; acquiring an indexof interest calculation template from the template storage 8011 based onthe received product history data, and calculating the index of interestfor the product to be queried using the product history data accordingto the index of interest calculation template. The result parsingprocessor 8013 is further arranged for sending the index of interest forthe product to be queried to the query terminal.

It should be understood that the operations and features described abovefor the method are also applicable to the product history queryapparatus and units comprised therein. The product history queryapparatus may be implemented in a browser of an electronic device orother security applications, or loaded to a browser of an electronicdevice or other security applications by downloading. The correspondingunits in the product history query apparatus may cooperate with thecorresponding units in the electronic device so as to implement thesolutions in the embodiments of this disclosure.

In some embodiments, the product history query apparatus according tothe embodiments of this disclosure may be implemented by an electronicdevice comprising a memory, a processor and a computer program stored onthe memory and executable on the processor. In the electronic device,the computer program, when executed by the processor, causes theprocessor to implement the product history query methods according tothe embodiments of this disclosure.

The divisions between the modules or units mentioned in the detaileddescription above are not compulsory. In fact, according to theembodiments of this disclosure, features and functions of two or moremodules or units described above may be embodied in one module or unit.On the contrary, features and functions of one module or unit describedabove may be further divided and embodied by a plurality of modules orunits.

FIG. 9 shows a schematic structure view of a computer system adapted toimplement a product history query solution according to an embodiment ofthis disclosure.

As shown in FIG. 9, the computer system comprises a central processingunit (CPU) 901, which may perform various appropriate actions andprocessing based on programs stored in the read-only memory (ROM) 902 orprograms loaded to the random-access memory (RAM) 903 from a storagepart 908. Various programs and data required for the operation of asystem 900 are stored in the RAM 903. CPU 901, ROM 902 and RAM 903 areconnected with each other via a bus 904. An input/output (I/O) interface905 is connected to the bus 904.

The following components are connected to the I/O interface 905: aninput part 906 including a keyboard, a mouse and so on; an output part907 including a cathode-ray tube (CRT), a liquid crystal display (LCD)for instance and a loudspeaker and so on; a storage part 908 including ahard disc and so on; and a communication part 909 of a network interfacecard including a LAN card, a modem, etc. The communication part 909executes communication processing via a network such as Internet. Adriver 910 is also connected to the I/O interface 905 as desired. Aremovable medium 911 such as a magnetic disc, an optical disc, amagneto-optical disc, a semiconductor storage and so on, is stalled onthe driver 910 as desired such that the computer program read outtherefrom is installed in the storage part 908 as desired.

In particular, the product history query solution according to theembodiments of this disclosure may be implemented as a computer softwareprogram. For example, an embodiment of this disclosure provides acomputer program product, comprising a computer program carried on amachine-readable medium, and the computer program comprises programcodes for implementing the method shown in the flow charts. In such anembodiment, the computer program may be downloaded and installed fromthe network via the communication part 909, and/or installed from theremovable medium 911. The computer program, when executed by the centralprocessing unit (CPU) 901, implements the above functions defined in thesystem of this disclosure.

It should be noted that, the computer-readable medium shown in thisdisclosure may be a computer-readable signal medium or a non-transitorycomputer-readable storage medium or any combination of the two. Thecomputer-readable storage medium may be, for example, but is not limitedto, an electrical, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any combination of theabove. More specific examples of the computer-readable storage mediummay include, but are not limited to: electrical connection with one ormore wires, portable computer disk, hard disk, random access memory(RAM), read-only memory (ROM), erasable programmable read-only memory(EPROM or flash memory), optical fiber, portable compact disk read-onlymemory (CD-ROM), optical storage device, magnetic storage device, or anysuitable combination of the above. In this disclosure, thecomputer-readable storage medium may be any tangible medium thatcontains or stores a program, and the program may be used by or incombination with an instruction execution system, apparatus, or device.In this disclosure, the computer-readable signal medium may contain adata signal propagating in a baseband or as a part of a carrier wave, inwhich computer-readable program codes are carried. This propagated datasignal may take many forms, including but not limited to electromagneticsignal, optical signal, or any suitable combination of the above. Thecomputer-readable signal medium may also be any computer-readable mediumother than a computer-readable storage medium, which computer-readablemedium may send, propagate, or transmit the program for use by or incombination with the instruction execution system, apparatus, or device.The program codes contained on the computer-readable medium may betransmitted by any suitable medium, including but not limited to:wireless, wire, optical cable, RF, etc., or any suitable combination ofthe above.

The units or modules described in the embodiments of this disclosure maybe implemented by software or by hardware. The described units ormodules may also be provided in the processor, for example, describedas: a processor comprising a query sub-module, wherein the terms ofthese units or modules do not constitute a limitation to the unit ormodule per se under certain circumstances, for example, the querysub-module may also be described as “a module for receiving a productquery request”.

In another aspect, this disclosure further provides a non-transitorycomputer-readable storage medium, and the computer-readable storagemedium may be contained in the electronic device described in the aboveembodiments; or it may exist independently without being integrated intothe electronic device. The computer-readable storage medium stores oneor more programs, and when the programs are executed by one or moreprocessors, the processors implement the product history query methodaccording to the embodiments of this disclosure.

What is described above is only preferred embodiments of this disclosureand explanations of the employed technical principles. Those skilled inthe art should understand that the scope of disclosure of the presentapplication is not limited by the technical solutions formed by aspecific combination of the above technical features, but instead, itshould also cover other technical solutions formed by any randomcombination of the above technical features or equivalent featuresthereof without deviating from the above disclosure concept, forexample, technical solutions formed by replacing the above features withthose disclosed in the present application or other technical featureshaving similar functions.

1. A method for querying a product history, comprising: receiving a product query request to a product graph database from a query device, the product query request including at least one product query parameter for a target product, the product graph database storing a relational map constructed based on a manufacturing process of the target product, the relational map describing entities including product entities and manufacturing entities and entity relations between the entities involved in the manufacturing process; querying the product graph database according to the product query parameter to obtain product history data of the target product, the querying comprising: searching for a product entity corresponding to the target product as a target product entity in the relational map according to the product query parameter; searching for one or more associated manufacturing entities of the target product entity according to the entity relations described by the relational map; obtaining the product history data based on the associated manufacturing entities; and sending a notification message to the query device to notify the query device of the obtained product history data.
 2. The method according to claim 1, wherein the searching for the product entity corresponding to the target product as the target product entity comprises: searching for a product entity of which property information matches with the product query parameter as the target product entity according to the entities described by the relational map, wherein the associated manufacturing entities are manufacturing entities having entity relations with the target product entity in the relational map.
 3. The method according to claim 2, wherein the product query parameter comprises at least one of a product identifier, a time range or a product arrival rate of the target product.
 4. The method according to claim 1, wherein the searching for a product entity corresponding to the target product as the target product entity comprises: searching for a product entity of which related property information matches with the product query parameter as a first product entity according to the entities or entity relations described by the relational map, and searching for a second product entity related to the first product entity according to the product entity relations between product entities described by the relational map, and determining the second product entity as the target product entity, wherein the product entity relation between the second product entity and the first product entity is correlating relation, and a product corresponding to the first product entity is made from a product corresponding to the second product entity.
 5. The method according to claim 4, wherein the product query parameter comprises a product arrival rate, the product arrival rate being a ratio of the number of first product entities that arrive at a specific station to the total number of first product entities, the first product entities being obtained from one same second product entity, and wherein the searching for the product entity corresponding to the target product as the target product entity comprises: searching for a first relation between product entities in the relational map according to the product arrival rate to obtain a first product entity that meets a requirement of the product arrival rate, the first relation indicating that the first product entity is obtained from the second product entity; and searching for a second relation between product entities in the relational map according to the first product entity to obtain a second product entity having the second relation with the first product entity as the target product entity, the second relation indicating that a defective result of the first product entity is related to the second product entity.
 6. The method according to claim 1, wherein prior to receiving the query request, the method further comprises creating the product graph database by performing operations comprising: obtaining original product history data from a manufacturing execution system (MES) executing the manufacturing process; processing the original product history data to obtain the product entities and the manufacturing entities, and constructing the entity relations according to transfer relations in the original product history data; and constructing the relational map according to the product entities, the manufacturing entities and the entity relations so as to create the product graph database.
 7. The method according to claim 6, wherein the product graph database supports a property graph model, and the property graph model comprises a metadata layer and a data layer, wherein the metadata layer is arranged for constructing the manufacturing entities and the entity relations between manufacturing entities, and wherein the data layer is arranged for constructing the product entities, the entity relations between product entities and the entity relations between product entities and manufacturing entities.
 8. The method according to claim 6, further comprising updating the product graph database by performing operations comprising: in response to at least one of the obtained entities and the constructed entity relations being changed with respect to the relational map stored in the product graph database, updating the relational map according to the changed entities and/or entity relations; and storing the updated relational map in the product graph database.
 9. The method according to claim 6, further comprising updating the product graph database by performing operations comprising: in response to a query to the product graph database comprising a multi-hop query across a plurality of entities in the relational map and an occurrence frequency of the multi-hop query being greater than a predetermined threshold, modifying the relational map to establish a directly connected entity relation between a start query entity and an end query entity corresponding to a start and an end of the multi-hop query; and storing the modified relational map in the product graph database.
 10. The method according to claim 1, wherein prior to querying the product graph database according to the product query parameter, the method further comprises: parsing the product query request to obtain the product query parameter; and invoking a corresponding query statement template according to the product query parameter to create a query statement for querying the product graph database, wherein querying the product graph database according to the product query parameter comprises querying the product graph database according to the query statement to obtain the product history data of the target product.
 11. The method according to claim 1, further comprising: generating an index of interest for the target product according to the product history data; and including the generated index of interest in the notification message.
 12. The method according to claim 11, wherein the generating the index of interest for the target product according to the product history data comprises: obtaining a corresponding index of interest calculation template according to the product history data; and calculating the index of interest for the target product using the product history data according to the index of interest calculation template.
 13. The method according to claim 1, wherein the generating the product history data according to the associated manufacturing entities comprises: combining the associated manufacturing entities according to a temporal property of the entity relations between the target product entity and the associated manufacturing entities to obtain the product history data.
 14. A system for querying a product history, comprising: a product graph database, configured to store a relational map constructed based on a manufacturing process of a target product, the relational map describing entities including product entities, manufacturing entities and entity relations between the entities involved in the manufacturing process; a product history query apparatus, configured to receive a product query request including at least one product query parameter for the target product, query the product graph database according to the product query parameter to obtain product history data corresponding to the target product by searching for a product entity corresponding to the target product as a target product entity in the relational map according to the product query parameter, search for one or more associated manufacturing entities of the target product entity according to the entity relations described by the relational map, and obtain the product history data according to the associated manufacturing entities.
 15. An electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the computer program, when executed by the processor, causes the processor to performing operations comprising: receiving a product query request to a product graph database from a query device, the product query request including at least one product query parameter for a target product, the product graph database storing a relational map constructed based on a manufacturing process of the target product, the relational map describing entities including product entities and manufacturing entities involved in the manufacturing process and entity relations between the entities; querying the product graph database according to the product query parameter to obtain product history data of the target product, the querying comprising: searching for a product entity corresponding to the target product as a target product entity in the relational map according to the product query parameter; searching for one or more associated manufacturing entities of the target product entity according to the entity relations described by the relational map; and obtaining the product history data according to the associated manufacturing entities; and sending a notification message to the query device to notify the query device of the obtained product history data.
 16. The electronic device according to claim 15, wherein the processor is further configured to obtain original product history data from a manufacturing execution system (IVIES) executing the manufacturing process, process the original product history data to obtain the product entities and the manufacturing entities, construct the entity relations according to transfer relations in the original product history data, and construct a relational map according to the product entities, the manufacturing entities and the entity relations so as to obtain the product graph database.
 17. The electronic device according to claim 15, wherein the processor is further configured to in response to a query to the product graph database comprising a multi-hop query across a plurality of entities in the relational map and an occurrence frequency of the multi-hop query being greater than a predetermined threshold, modify the relational map to establish a directly connected entity relation between a start query entity and an end query entity corresponding to a start and an end of the multi-hop query, and store the modified relational map in the product graph database.
 18. The electronic device according to claim 15, wherein the processor is further configured to obtain a corresponding query statement template from a template storage according to the product query parameter, create a query statement using the product query parameter according to the query statement template, and query the product graph database according to the query statement.
 19. The electronic device according to claim 15, wherein the processor is further configured to receive product history data of the target product returned by the product graph database; obtain an index of interest calculation template from a template storage according to the product history data, and calculate an index of interest for the target product using the product history data according to the index of interest calculation template.
 20. A non-transitory computer-readable storage medium, having a computer program stored thereon, wherein the computer program, when executed by a processor, causes the processor to implement the method according to claim
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